Variational Graph Auto-Encoders,变分图自编码器 Variational Graph Auto-Encodersarxiv.org/pdf/1611.07308.pdf codespaperswithcode.com/paper/variational-graph-auto-encoders 变分图自编码器(VGAE)是一种基于变分自编码器的图结构数据上的无监督学习
In this study, we present a deep learning framework with variational graph auto-encoder for miRNA-disease association prediction (VGAE-MDA). VGAE-MDA first gets the representations of miRNAs and diseases from the heterogeneous networks constructed by miRNA-miRNA similarity, disease-disease similarity,...
论文标题:Variational Graph Auto-Encoders 论文作者:Thomas Kipf, M. Welling 论文来源:2016, ArXiv 论文地址:download 论文代码:download 1 Introduce 变分自编码器在图上的应用,该框架可以自行参考变分自编码器。 2 Method 变分图自编码器(VGAE ),整体框架如下: ...
本文介绍了变分图自动编码器(VGAE),这是一个基于变分自动编码器(VAE)的图结构数据的无监督学习框架。该模型利用了潜在变量学习,并学习无向图的可解释的潜在表示。本文使用一个GCN编码器和一个简单的内积解码器来构成该模型。并且该模型在引文网络中链路预测任务上取得了优越结果。
3. Variational Graph Autoencoders 结构框架 输入是一个邻接矩阵和一个特征矩阵,产生潜变量Z, 最后输出是一个新的邻接矩阵。 第一个gcn层产生一个新的低维得特征矩阵。 第二层GCN产生 将两层合并,得到均值和标准差, 。 然后我们产生Z, 解码是通过潜在变量Z的内积,输出是一个新的邻接矩阵, ...
This model combines the probabilistic interpretation of CCA with an autoencoder architecture based on graph convolutional neural network layers. Experiments with the proposed method are conducted on classification, clustering, and recommendation tasks on real datasets. The algorithm is competitive with state...
6.PyTorch Geometric tutorial: Graph Autoencoders & Variational Graph Autoencoder 0播放 5.Pytorch Geometric tutorial: Aggregation Functions in GNNs 1播放 4.Pytorch Geometric tutorial: Convolutional Layers - Spectral methods 1播放 3.Pytorch Geometric tutorial: Graph attention networks (GAT) implementation ...
Variational graph encoders effectively combine graph convolutional networks with variational autoencoders, and have been widely employed for biomedical graph-structured data. Lam and colleagues developed a framework based on the variational graph encoder, NYAN, to facilitate the prediction of molecular prop...
Chen, Dongming, et al. “Network embedding algorithm taking in variational graph autoencoder.” Mathematics 10.3 (2022): 485. 属性网络在现实世界中被广泛的用于建模实体间的连接,其中节点的联通边表示对象之间的关系以及关于节点本身的描述中节点的属性信息。举了3个例子: ...
Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data. However, due to the i.i.d. assumption, VAEs only optimize the singleton variational distributions and fail to account for the correlations between data points, which might be crucial for le...